Small Discussion About Pyramid Python?

Pyramid is a Python web application framework. It is designed to make creating web applications easier. It is open source. A pyramid is fully compatible with Python 3.

With Pyramid, we can write very small applications without needing to know a lot. And by learning a bit more, you can write very large applications too.

Pyramid will allow you to become productive quickly and will grow with you. It won't hold you back when your application is small, and it won't get in your way when your application becomes large. Other application frameworks seem to fall into two non-overlapping categories: those that support "small apps" and those designed for "big apps".

Pyramid can automatically detect changes you make to template files and code, so your changes are immediately available in your browser. You can debug using plain old print() calls, which will display to your console.

Pyramid has a debug toolbar that allows you to see information about how your application is working right in your browser. See configuration, installed packages, SQL queries, logging statements and more.

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Statsmodels is a Python package that allows users to explore data, estimate statistical models, and perform statistical tests. Statsmodels is built on top of the numerical libraries NumPy and SciPy, integrates with Pandas for data handling and uses Patsy for an R-like formula interface.

Statsmodels is part of the scientific Python stack that is oriented towards data analysis, data science and statistics. Statsmodels is built on top of the numerical libraries NumPy and SciPy, integrates with Pandas for data handling and uses Patsy[3] for an R-like formula interface. Graphical functions are based on the Matplotlib library. Statsmodels provides the statistical backend for other Python libraries. Statmodels in free software released under the Modified BSD (3-clause) license.

Features

Linear regression models:

Mixed Linear Model with mixed effects and variance components

GLM: Generalized linear models with support for all of the one-parameter exponential family distributions

Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research.

Supports both convolutional networks and recurrent networks, as well as combinations of the two.

Runs seamlessly on CPU and GPU.

Main Benefits

User friendliness. Keras is an API designed for human beings, not machines. It puts user experience front and center. Keras follows best practices for reducing cognitive load: it offers consistent & simple APIs, it minimizes the number of user actions required for common use cases, and it provides clear and actionable feedback upon user error.

Modularity. A model is understood as a sequence or a graph of standalone, fully-configurable modules that can be plugged together with as few restrictions as possible. In particular, neural layers, cost functions, optimizers, initialization schemes, activation functions, regularization schemes are all standalone modules that you can combine to create new models.

Easy extensibility. New modules are simple to add (as new classes and functions), and existing modules provide ample examples. To be able to easily create new modules allows for total expressiveness, making Keras suitable for advanced research.

Work with Python. No separate models configuration files in a declarative format. Models are described in Python code, which is compact, easier to debug, and allows for ease of extensibility

Scikit-learn (formerly scikits.learn) is a free software machine learning library for the Python programming language.[3] It features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy.

Features:

Simple and efficient tools for data mining and data analysis

Accessible to everybody, and reusable in various contexts

Built on NumPy, SciPy, and matplotlib

Open source, commercially usable - BSD license

scikit-learn comes with a few standard datasets, for instance the iris and digits datasets for classification and the boston house prices dataset for regression.

Scikit-learn is largely written in Python, with some core algorithms written in Cython to achieve performance. Support vector machines are implemented by a Cython wrapper around LIBSVM; logistic regression and linear support vector machines by a similar wrapper around LIBLINEAR.

Bottleis a WSGI micro web-framework for the Python programming language. It is designed to be fast, simple and lightweight, and is distributed as a single file module with no dependencies other than the Python Standard Library. The same module runs with Python 2.5+ and 3.x.

It offers request dispatching (routes) with URL parameter support, templates, a built-in web server and adapters for many third-party WSGI/HTTP-server and template engines.

It is designed to be lightweight, and to allow development of web applications easily and quickly.

Bottle is a fast, simple and lightweight WSGI micro web-framework for Python. It is distributed as a single file module and has no dependencies other than the Python Standard Library.

Routing: Requests to function-call mapping with support for clean and dynamic URLs.Templates: Fast and pythonic built-in template engine and support for mako, jinja2 and cheetah templates.Utilities: Convenient access to form data, file uploads, cookies, headers and other HTTP-related metadata.Server: Built-in HTTP development server and support for paste, fapws3, bjoern, gae, cherrypy or any other WSGI capable HTTP server.

Features

Single file which runs with both Python 2.5+ and 3.x

Can run as a standalone web server or be used behind ("mounted on") any web server which supports WSGI

Dash is a Python framework for building analytical web applications. No JavaScript required.

Written on top of Flask, Plotly.js, and React.js, Dash is ideal for building data visualization apps with highly custom user interfaces in pure Python. It's particularly suited for anyone who works with data in Python.

Through a couple of simple patterns, Dash abstracts away all of the technologies and protocols that are required to build an interactive web-based application. Dash is simple enough that you can bind a user interface around your Python code in an afternoon.

Dash apps are rendered in the web browser. You can deploy your apps to servers and then share them through URLs. Since Dash apps are viewed in the web browser, Dash is inherently cross-platform and mobile-ready.

Benefits

1) Lightweight - Dash apps require very little boilerplate to get started: An app like this weighs in at just 40 lines of pure Python. Dash provides direct control2) Direct Control - Dash provides a simple interface for tying UI controls, like sliders, dropdowns, and graphs, with your Python data analysis code. Dash is Composable and Modular3) Completely Customizable - Every aesthetic element of a Dash app is customizable. Dash apps are built and published in the Web, so the full power of CSS is available.